EP2856710B1 - Echtzeit-netzwerküberwachung und teilnehmeridentifizierung mit einer on-demand-vorrichtung - Google Patents

Echtzeit-netzwerküberwachung und teilnehmeridentifizierung mit einer on-demand-vorrichtung Download PDF

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Publication number
EP2856710B1
EP2856710B1 EP13797712.0A EP13797712A EP2856710B1 EP 2856710 B1 EP2856710 B1 EP 2856710B1 EP 13797712 A EP13797712 A EP 13797712A EP 2856710 B1 EP2856710 B1 EP 2856710B1
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European Patent Office
Prior art keywords
data flow
network
flow
subscriber
user device
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English (en)
French (fr)
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EP2856710A1 (de
EP2856710A4 (de
Inventor
Erik R. Swenson
Nitin Bhandari
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Performance And Privacy Ireland Ltd
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Performance And Privacy Ireland Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/11—Identifying congestion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/04—Processing captured monitoring data, e.g. for logfile generation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0876—Network utilisation, e.g. volume of load or congestion level
    • H04L43/0882—Utilisation of link capacity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/12—Avoiding congestion; Recovering from congestion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/10—Flow control; Congestion control
    • H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
    • H04L47/2441—Traffic characterised by specific attributes, e.g. priority or QoS relying on flow classification, e.g. using integrated services [IntServ]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/70—Admission control; Resource allocation
    • H04L47/83—Admission control; Resource allocation based on usage prediction
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client 
    • H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
    • H04N21/647—Control signaling between network components and server or clients; Network processes for video distribution between server and clients, e.g. controlling the quality of the video stream, by dropping packets, protecting content from unauthorised alteration within the network, monitoring of network load, bridging between two different networks, e.g. between IP and wireless
    • H04N21/64723—Monitoring of network processes or resources, e.g. monitoring of network load
    • H04N21/64738—Monitoring network characteristics, e.g. bandwidth, congestion level
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

Definitions

  • the disclosure generally relates to improving user experience on a network, and more specifically, to monitoring bandwidth consumption of the many devices connected to a given node in the network.
  • US 2002/0172222 A1 describes a system and method for management of a distributed data processing system that is able to monitor multiple sources of network packets on various subnets within the distributed data processing system.
  • EP 1944922 A1 describes a method of providing quality of service in an IP-based real-time communication system.
  • WO 2011/047335 A1 describes a system and method for adaptively streaming multimedia content, including monitoring at least one parameter of a user terminal.
  • US 2008/0273591 A1 describes methods and apparatus for dynamically adjusting capacity allocation, for example to a group of services in a multimedia distribution network.
  • Embodiments disclosed include a network controller system for real-time data gathering on the state of existing network traffic flows and mapping flow data to respective users in the network to predict available bandwidth and level of congestion.
  • the network controller system By gathering a history of flow statistics in the network, the network controller system establishes a relationship between base stations (or other network segments) and their capability to deliver the amount of data typically required by a particular user of the network.
  • the very recent history of network flows can be used to predict the near future congestions in a substantially real-time fashion.
  • the history of flow statistics can be used to build a long-term map of user behavior on the network, which can more effectively predict on demand data delivery requirements for the collection of users utilizing a given network access point in a consistent manner.
  • the network controller keeps a flow state database, which groups flow data in a number of ways, such as on per station/cell tower, per subscriber, per time-of-day, or per geography area basis.
  • database can be queried to estimate the network congestion level for new flows to determine whether existing, new or future flows require optimizations in order to maintain the desired level of user satisfaction.
  • an on-demand network monitoring method is adopted to gather data about network flows as they traverse the network.
  • network flows can be monitored selectively or on-demand based on the types of the content carried in the flows.
  • the network monitoring can also be performed selectively at inline level, as well as out-of-band to improve efficiency.
  • Both TCP and UDP flows are monitored to gather information about the state of the network, such as the average network throughput for each flow and end-to-end latency between, for example, a client device and an origin server providing multimedia content to the client device.
  • the system tracks the number of bytes sent (and in some embodiments acknowledged). In TCP, the current window size may also be tracked.
  • a flow statistics database which can be indexed by subscriber identification (ID), cell tower (base station), and network segment etc. As many flow records accumulate, this database represents both historical and current network condition and capacity for delivering data. Network throughput can be measured by calculating an average number of bytes delivered over a period of time. Steps may be taken to filter out spurious data from small flows with size less than a certain threshold that, when measured, cause very noisy results in measuring bandwidth and/or latency. For example, any flow having delivery time of less than 500 ms can be filtered.
  • large objects such as video files and data traversing the network are monitored and paced. Rather than just measuring the bandwidth associated with large object delivery, estimates for future bandwidth needs of the network are determined based on the measurements.
  • large objects may be selectively optimized to preserve network throughput.
  • streaming bit rate of the flow provides a parameter, which can be compared against network capacity to determine if the network is able to sustain the required level of throughput. It is often advantageous to pace the transfer speed to not exceeding a known cap. If a flow can be delivered at a rate between the minimum level needed to keep the video from stalling and the upper limit of the pacing limit, then the network segment on which the flow traverses is said to be capable of sustain the bit rate required for the flow.
  • a reasonable size threshold for separating a large object from a small object can be set between 512kB to 1MB, and 50kB and up for medium-sized objects. Other values are also possible.
  • the bandwidth attained by a single flow may be sufficient to determine the capacity of the network segment(s) the flow traverses. Therefore, with a fairly small deployment of network controller(s), an accurate detection of key network congestion points can be derived. Specifically, one does not need to monitor every flow going through a network segment to detect congestions. Since video currently comprises around 50% of the traffic on a network but only around 5% of total flows, a relatively small number of flow samples of large objects can map a statistically significant portion of the network.
  • the network controller selects large video or image flows through an on-demand video optimizer to optimize large object delivery and thus available network bandwidth. With the controller and/or optimizer to intelligently and selectively handle the measurement and optimization, these operations are offloaded from network routing appliances.
  • FIG. ( Figure) 1 illustrates a high-level block diagram of an example communications environment 100 for selective on-demand real-time network monitoring and subscriber identification.
  • the environment 100 comprises user devices 110, an origin server 160, a steering device 130, a network controller 140, a video optimizer 150, and a network 120.
  • the network 120 is a communication network that transmits data between the user devices 110, the steering device 130 and the origin server 160 and/or the video optimizer 150.
  • the network 120 includes wireless network and the Internet.
  • a network efficiency strategy that aspires to keep capital expenditure from outpacing revenues has to be balanced with demands from consumers for better user experiences that rely increasingly on higher data usage.
  • mobile operators are employing a variety of tools to manage capacity including data usage caps, Wi-Fi offload and intelligent optimization.
  • the environment 100 demonstrates such a solution that provides a unified foundation with deep session intelligence, integrated services management, and dynamic adaptability to fit any service offering.
  • the network controller 140 and the video optimizer 150 deliver a world-class media optimization solution that brings a surgical capacity advantage to wireless operators as well as Internet service providers with better peak capacity savings than alternative solutions.
  • the user devices 110 are computing devices with network capabilities. Oftentimes, for example, the user devices 110 are wireless enabled mobile computing device with a web browser and media display capability.
  • the user devices 110 as mobile computing devices may include laptops, netbooks, tablets, smart telephones, or personal digital assistants (PDAs). While only two user devices 110A and 110B are illustrated in FIG. 1 , the environment 100 may include thousands or millions of such devices.
  • the web browsers may be software applications running on mobile devices 110 for retrieving web content from the origin server 160 and presenting the web content on a display coupled to the mobile device. Web content accessed by the user devices 110 include text, images, audio and video content.
  • the multimedia content can be played back by the browsers, for example, HTML5 compatible browsers, plug-in or a standalone media player.
  • the browsers can also invoke the media players or plug-ins available on the user devices 110 and passes images, audio and/or video to the media player or plug-in for playback.
  • the steering device 130 may be a load balancer or a router located between the user device 110 and the network 120.
  • the steering device 130 provides the user device 110 with access to the network and thus, provides the gateway through which the user device traffic flows onto the network and vice versa.
  • the steering device 130 categorizes traffic routed through it to identify flows of interest for further inspection at the network controller 140.
  • the network controller 140 interfaces with the steering device 130 to coordinate the monitoring and categorization of network traffic, such as identifying large and small objects in HTTP traffic flows.
  • the steering device 130 receives instructions from the network controller 140 based on the desired criteria for categorizing flows of interest for further inspection.
  • information on the wireless/cellular user devices 110 side is often not available at the steering device 130 that sits between the cellular network and the wired Internet. For example, there is often no information about the identifiers of the towers associated with the mobile devices 110. Tower association information only broadcasted when the mobile devices first attached to the network. In addition, user devices 110 do not usually report any identification information except their IP addresses. Therefore, monitoring of the network traffic and detection of the congestion is automated and managed by the detector 140 so that network can be optimized for end user's experience without the mobile user's knowledge.
  • the network controller 140 is an "out-of band" computer server that interfaces with the steering device 130 to selectively inspect user flows of interest.
  • the network controller 140 may further identify user flows (e.g., among the flows of interest) for optimization.
  • the network controller 140 may be implemented at the steering device 130 to monitor traffic.
  • the network controller 140 is coupled to and communicates with the steering device 130 for traffic monitoring and optimization. When queried by the steering device 130, the network controller 140 determines if a given network flow should be ignored, monitored further or optimized.
  • optimization of a flow is often decided at the beginning of the flow because it is rarely possible to switch to optimized content mid-stream once non-optimized content delivery has begun.
  • the network controller 140 may determine that existing flows associated with a particular subscriber or other entity should be optimized.
  • new flows e.g., resulting from seek requests in media, new media requests, resume after pause, etc.
  • the network controller 140 uses the network state as well as historical traffic data in its decision for monitoring and optimization. Knowledge on the current network state, such as congestion, deems critical when it comes to data optimization.
  • the historical network traffic data includes information such as subscriber information, the cell towers to which the user devices attached, routers through which the traffic is passing, geography regions, the backhaul segments, and time-of-day of the flows. For example, in a mobile network, the cell tower to which a user device is attached can be most useful, since it is the location where most congestion occurs due to limited bandwidth and high cost of the radio access network infrastructure.
  • the network controller 140 looks into the historical traffic data for the average of the bandwidth per user at the particular cell tower. The network controller 140 can then estimate the amount of bandwidth or degree of congestion for the new flow based on the historical record.
  • the video optimizer 150 is a computer server that provides video and image optimization and delivers optimized video and image content to the user devices 110 via the network 120.
  • the video and image optimization is an on-demand service provided through the transcoding of the video and image content. For example, when a user device attempts to retrieve video from the origin server 160, the network controller 140 may decide that the flow meets certain criteria for content optimization. The network controller 140 then redirected the user devices 110 to the video optimizer 150 to retrieve the optimized content.
  • the video optimizer 150 receives information in the redirect request from the user devices 110 or from the network controller 140 about the video or image content to be optimized and retrieve the video or image content from the corresponding origin server 160 for optimization and subsequent delivery to the user devices 110.
  • Video optimization focus on the video optimization because video is of far greater importance than all other traffic types when network congestion is considered.
  • Video traffic makes up around half of all network traffic - and the percentage is growing every year. Therefore, optimizing video traffic massively reduces congestion in the network.
  • Video flows or streams are also long lived, having large packet size, and demanding high bitrates, monitoring video streams is an effective ways of detecting congestion in the network.
  • video streams require steady and consistent bandwidth, they are among first to be impacted when congestion occurs and available network bandwidth reduces.
  • web page text and images are generally not affected under mild network congestion with unnoticeable longer load times.
  • Video optimization differs in one key aspect to optimizing other web content: it is long form, and the optimization policy decisions made at the beginning of a video have significant impact on the ability to deliver a consistent experience for the length of the video.
  • the video optimizer 150 and the origin server 160 are typically formed of one or more computers. While only one server of each video optimizer 150 and origin server 160 is shown in the environment 100 of FIG. 1 , different embodiments may include multiple web servers and video servers operated by a single entity or multiple entities. In other embodiments, a single server may also provide different functionalities, such as delivering web content as a web server, as well as serving optimized video content.
  • FIG. 2 is a block diagram illustrating components of an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller) to implement the disclosed system for on-demand real-time network monitoring and subscriber identification.
  • FIG. 2 shows a diagrammatic representation of a machine in the example form of a computer system 200 within which instructions 224 (e.g., software) for causing the machine to perform any one or more of the methodologies discussed herein may be executed.
  • the machine operates as a standalone device or may be connected (e.g., networked) to other machines.
  • the machine may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment.
  • the machine may be a server computer, a client computer, a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a smart phone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions 224 (sequential or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA personal digital assistant
  • STB set-top box
  • a cellular telephone a smart phone
  • web appliance a web appliance
  • network router switch or bridge
  • the example computer system 200 includes one or more processors 202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application specific integrated circuits (ASICs), one or more radiofrequency integrated circuits (RFICs), or any combination of these), a main memory 204, and a static memory 206, which are configured to communicate with each other via a bus 208.
  • the computer system 200 may further include graphics display unit 210 (e.g., a plasma display panel (PDP), a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)).
  • processors 202 e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), one or more application specific integrated circuits (ASICs), one or more radiofrequency integrated circuits (RFICs), or any combination of these
  • main memory 204 e.g., a main memory 204
  • the computer system 200 may also include alphanumeric input device 212 (e.g., a keyboard), a cursor control device 214 (e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument), a storage unit 216, and a network interface device 220, which also are configured to communicate via the bus 208.
  • alphanumeric input device 212 e.g., a keyboard
  • a cursor control device 214 e.g., a mouse, a trackball, a joystick, a motion sensor, or other pointing instrument
  • storage unit 216 e.g., a hard disk drive, a hard disk drive, or other pointing instrument
  • network interface device 220 e.g., a network interface device
  • the storage unit 216 includes a machine-readable medium 222 on which is stored instructions 224 (e.g., software) embodying any one or more of the methodologies or functions described herein.
  • the instructions 224 e.g., software
  • the instructions 224 may also reside, completely or at least partially, within the main memory 204 or within the processor 202 (e.g., within a processor's cache memory) during execution thereof by the computer system 200, the main memory 204 and the processor 202 also constituting machine-readable media.
  • the instructions 224 (e.g., software) may be transmitted or received over a network 110 via the network interface device 220.
  • machine-readable medium 222 is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions (e.g., instructions 224).
  • the term “machine-readable medium” shall also be taken to include any medium that is capable of storing instructions (e.g., instructions 224) for execution by the machine and that cause the machine to perform any one or more of the methodologies disclosed herein.
  • the term “machine-readable medium” includes, but not be limited to, data repositories in the form of solid-state memories, optical media, and magnetic media.
  • the network controller 140 allows network operators to apply fine granular optimization policies to ensure high quality of experience (QoE) based on cell tower congestion, device types, subscriber profiles and service plans with lower hardware and software costs.
  • the architecture of the network controller 140 provides an excellent fit for the net neutrality guideline of "reasonable network management", and better compliance to the copyright law (DMCA) than solutions that rely on long-term caching.
  • DMCA copyright law
  • the network controller 140 also selectively monitors and optimizes only a subset of traffic that benefits from optimization the most, thus achieving both scalability and efficiency for optimization at a competitive price-point.
  • the core element of the network controller 140 lies in its mechanisms for congestion detection and mitigation, which allows optimization resources to be utilized in the most efficient and surgical manner.
  • the network controller 140 comprises a flow analyzer 312, a policy engine 314, a steering device interface 316, a video optimizer redirector 318, a flow cache 322, and a subscriber log 324.
  • the network controller 140 may include additional, fewer, or different components for various applications. Conventional components such as network interfaces, security functions, failover servers, management and network operations consoles, and the like are not shown so as to not obscure the details of the system architecture.
  • the flow analyzer 312 monitors large flows in the network, analyzes collected flow statistics to determine network throughput, and accordingly selects flows to be optimized.
  • the flow analyzer 312 does not need to see all the flows in order to make an accurate estimate of network conditions.
  • the flow analyzer 312 processes the traffic statistics stored in the flow cache 322 and user information stored in the subscriber log 324, for example, by associating network flows identified by source IP addresses to a mobile subscriber or user, which is identified by his or her current subscriber ID or device ID.
  • the user flows are also mapped to a congestion level at the current sub-network (e.g., a cell with which the user devices are associated), so that an optimization decision can be made at the beginning of the data transmission.
  • congestion detection for mobile networks focuses on extracting network status actively from the radio access network (RAN) via dedicated hardware probes.
  • a probe for each sub-section of the network e.g., a mobile cell
  • the network statistics such as congested cell sites, level of congestion (e.g., none, low, medium, high), and active subscribers currently associated with the cell, to one or more network monitoring servers.
  • level of congestion e.g., none, low, medium, high
  • the network controller 140 collects real-time statistical data on the network flows from core network side without probes deployed in the RAN network.
  • the statistical data is stored and compared against historical flow data to estimate level of congestion and available network bandwidth.
  • the network controller 140 samples only large flows involving media objects such as videos and images above a certain size (e.g., above 50kB).
  • the network controller 140 can choose to be a pass-through device to monitor the large flows as well as to determine whether to optimize the flows. Measuring only larger flows has the advantage to mitigate corruptions caused by origin server latency and network glitches.
  • the flow analyzer 312 determines near real-time network condition by taking into consideration all the information accumulated, such as over the last few minutes.
  • the most powerful application of this analysis comes from the near real-time estimation of bandwidth consumption on a per user basis, which allows the flow analyzer 312 to generate a data footprint or behavior pattern of a particular user. For example, it is possible to detect a situation where a user is experiencing difficult network conditions while attached to a non-congested tower, if the user is at the edge of the cell or behind walls in a building causing poor connectivity.
  • flow analyzer 312 helps the flow analyzer 312 to determine repeating patterns and heat-maps of certain network sections and to predict when they are under congestion.
  • the flow statistics stored in the flow cache 322 can be mapped against traffic categories for analysis, for example, long-term running averages of video flow bandwidth help determine suitability for optimization.
  • estimated bandwidth per user (or per cell-ID, per tower, or per router) over time may be metrics calculated by the flow analyzer 312 in order to determine short term needs for optimization.
  • the flow analyzer 312 may determine to being optimizing flows associated with a particular cell-ID (or those flows for identified high-bandwidth users on the cell-ID) in response to a threshold number of high-bandwidth users connecting to a same cell tower corresponding to the cell-ID.
  • the reason why flow analyzer 312 selectively monitors large flows lies in the realization that TCP statistics for small objects, which make up most web flows, can be misleading and cause huge errors in throughput estimations.
  • the steering device interface 316 interacts with an external routing appliance, such as the steering device 130 to divert portions of the network traffic (e.g., large object network flows).
  • an external routing appliance such as the steering device 130 to divert portions of the network traffic (e.g., large object network flows).
  • Existing routing appliances in most carrier networks are designed to handle large amounts of network traffic. They are not, however, ideal devices to operate for monitoring and analysis individual flows.
  • the network controller 140 may communicate with the external routing appliances, such as the steering device 130, to steer a portion of network traffic to the network controller 140 when certain conditions are met.
  • network flows of interest to the network controller 140 contain larger media objects, such as videos and images.
  • the smaller flows such as web page and text information, are not exchanged over the steering device interface 316.
  • the flow cache 322 stores monitored flow information, which is updated for a flow with each associated transaction from the steering device 130.
  • data in the flow cache is stored in a map indexed by a hash, which can be up to 64-bit or longer.
  • An entry in the flow cache map may be organized as a linked list to allow hash collisions.
  • fewer bits in the hash index can also be used to speed up binary search in the flow cache map.
  • 64-bit hash index which requires at worst 64 steps to find a node
  • the hash index can be reduced to 16-24 bits. There will be more hash collisions, hence the longer linked list.
  • Other embodiments may use other type of maps or binary trees instead of the linked list to further optimize the hash collision searches.
  • the subscriber log 324 stores user or subscriber information, such as user or subscriber identifications and their device information.
  • the subscriber and device information is provided to the subscriber log 324 by the administrators or operators of the carrier or service provider networks.
  • the subscriber or the device information of the carrier networks e.g., mobile ISPs
  • the network controller 140 This makes bandwidth measurement more difficult since multiple users' devices may share a single IP address using the network address translation (NAT) protocol. Accordingly, algorithms that separate multiple users sharing an IP address can be implemented by the flow analyzer 312 to determine the amount of bandwidth available to individual users.
  • NAT network address translation
  • the data stored in the flow cache 322 and the subscriber log 324 is used for bandwidth monitoring and user identification by the flow analyzer 312.
  • the flow analyzer 312 scans through all the entries in the flow cache map. Since other operations, such as an addition of a new flow cache entry or an update to flow statistics, may compete with the scanning and cause data corruption, the flow analyzer 312 locks the flow cache while performing user identification and bandwidth monitoring. To keep the locking minimum, the scanning can be suspended after a pre-determined number of operations. The flow analyzer 312 can resume where it left off on the next pass. This is achieved by keeping, for example, an iterator of the last map position scanned. In case another operation causes changes to the iterator, the iterator can be adjusted accordingly to make sure it is always valid.
  • the policy engine 314 defines policies for optimizing large flows with media objects to mitigate network congestion. Detecting and acting on congestion in the network, the design focus of the network controller 140 is built on this very flexible policy engine.
  • the policy engine 314 is capable of taking virtually any input, either deduced from HTTP headers and payload (e.g., through RADIUS/Gx interface), or provided by the network infrastructure via API, and making decisions on how to apply optimization based on individual or a combination of these inputs.
  • the optimization policies can be applied to large flows all the time or on a time-of-day basis, a per user basis, and/or depending on the network condition.
  • the policy engine 314 can be configured to apply optimization based on time-of-day for different segments of the network.
  • the time-of day configuration may be determined based on historical flow statistic data stored in the flow cache 322 using the knowledge of patterns in network condition and bandwidth consumption of certain network segments during specific time-of-day or week. For instance, if network-wide "high congestion" occurs from 7 pm to 10 pm every day, a policy can be set to optimize video during the time period with a source video bit rate over 225 kbps. If network-wide "medium congestion” occurs from 3 pm to 7 pm every day, to optimize video with a source bit rate over 300 kbps. It is also possible to manually overwrite the time-of-day policy at any time when the congestion happens.
  • the policy engine 314 can adjust optimization policies not only based on network congestion, but also based on overall optimization capacity. Depending on the optimization capacity installed to serve the network, optimization requests may be "oversubscribed" at the optimization servers. As a means of monitoring the optimization capacity, "health check" messages are exchanged between the network controller 140 and video optimizer 150 server pools. If an optimizer pool is fully utilized, the network controller 140 will be notified. As the optimizer pool approaches full utilization, the policy engine 314 can dynamically adjust bit rate threshold of the flows to be optimized to accommodate only the largest flows.
  • the policy engine 314 optimizes any video flow with bit rate of 15% or higher than the targeted rate (i.e., about 260 kbps). But once the optimization capacity of the video optimizer 150 reached 85%, the policy engine 314 increases the threshold so that only videos with a bit rate of 300 kbps and above are optimized to maximize the overall bandwidth savings. As the video optimizer 150 reaches even higher utilization, the threshold level will be further increased, for instance, only HD videos will be optimized, since the greatest user experience benefit comes from the "heaviest" videos.
  • Full geo-redundancy and geo-load sharing are also supported at the policy engine 314. For example, if local pool of optimizers is fully utilized, the optimization request can be passed to a remote optimizer pool.
  • the policy engine 314 allows the current network condition as well as the available capacity to determine the type or size of video flows to be optimized. This opens up the possibility to acquire optimization resources that fit a budget and keep the optimization servers at full utilization all the time, knowing that the network controller automatically prioritizes the video to achieve best efficiency.
  • the video optimizer redirector 318 generates a redirect request to a URL pointing to the video optimizer 150 if the video is deemed to be transcoded.
  • the URL may contain at least one of a video resolution, a video bit rate, a video frame rate divisor, an audio sample rate and number of channels, an audio bit rate, a source URL, a user agent of a client, a source domain cookie and any other authentication data by the video optimizer 150.
  • the video optimizer redirector 318 rewrites the original response with the HTTP redirect and sets the location header to the new URL. This causes the user devices 110 to issue a new request to the video optimizer 150.
  • the video optimizer redirector 318 also has the logic to look for incoming URLs generated by itself so that they are not intercepted again.
  • FIGS. 4A and 4B each illustrates one embodiment of an example working mode of the network controller for providing selective on-demand real-time network monitoring and subscriber identification.
  • Shown with the network controller 140 are the user device 110, the steering device 130, and the origin server 160.
  • the network controller 140 is coupled to the steering device 130 through the steering device interface 316.
  • the network controller 140 and the steering device 130 communicate with each other using the Internet content adaption protocol (ICAP).
  • ICAP Internet content adaption protocol
  • the steering device interface 316 executes an ICAP server 406, which interacts with an ICAP client 404 running on the steering device 130. Similar or different protocols may be used for communication between the network controller 140 and the steering device 130 in other embodiments.
  • the Internet content adaption protocol is a lightweight protocol aimed at executing a simple remote procedure call on HTTP messages.
  • ICAP leverages edge-based devices to help deliver value-added services using transparent HTTP proxy caches.
  • Content adaptation refers to performing the particular value added service, such as content manipulation or other processing, for the associated HTTP client request/response.
  • ICAP clients pass HTTP messages to ICAP servers for transformation or other processing.
  • the ICAP server executes its transformation service on the HTTP messages and sends back responses to the ICAP client.
  • a cache that can proxy all client transactions and process them through ICAP servers, which may focus on specific functions, such as ad insertion, virus scanning, content translation, language translation, or content filtering.
  • ICAP servers such as those utilized by the network controller 140, handle these tasks to off-load value-added services from network devices including an ICAP client, such as the steering device 130.
  • processing infrastructure e.g., optimization services and network controllers
  • network traffic flows from the user device 110 through the steering device 130 and arrive at the origin server 160 over the network request path.
  • a browser on the user device 110 may request web content from the origin server 160.
  • a HTTP request message initiated at the user device 110 is forwarded to the steering device 130 over the network link 411.
  • a data switch 402 inside the steering device 130 then relays the request message to the origin server 160 over the network link 412.
  • network traffic originated from the origin server 160 flows through the steering device 130 back to the user device 110 over the network response path.
  • the origin server 160 responds to the user request by sending web content over the network link 413 to the steering device 130, which forwards the web content to the user device 110 over the network link 416.
  • network links 411 and 416 are two opposite directions on the same physical link, so are the network link pair 414 and 415.
  • the network link pair 412 and 413 may or may not share the same network path because traffic between the steering device 130 and origin server 160 on opposite directions may be routed differently over one or more routers.
  • the steering device 130 monitors network responses, it is looking for flows that match one or more signatures for video and images.
  • the steering device 130 forwards the HTTP request and a portion of the HTTP response to the network controller 140 over the ICAP client interface 404.
  • the flow analyzer 312 of the network controller 140 After receiving the request and the portion of response at the ICAP server interface 406, the flow analyzer 312 of the network controller 140 performs a deep flow inspection to determine if the flow is worth bandwidth monitoring and/or user detection.
  • the flow inspection performed by the flow analyzer 312 may determine if the flow indeed contains large or medium object (e.g., larger than 50kB), and/or if the source IP address of the flow is from a user or a group of users that are required to be monitored by policies.
  • the flow analyzer 312 may also determine if the flow needs to be optimized based on historical flow statistical data.
  • the steering device 130 is notified to steer the flow through the network controller 140.
  • This is known as the "continue" working mode for bandwidth monitoring.
  • the network controller 140 interfaces with the steering device 130 to function, on-demand, as a traditional inline network element for flows deemed of interest.
  • the network controller 140 ingests the network flow for inspection and subsequently forwards the network flow on the network response path.
  • the origin server 160 responds to the user request by sending video or images over the network link 413 to the steering device 130, which forwards the video or images to the network controller 140 over a network link 414.
  • the video or images are returned to the steering device 130 over a network link 415, which transmits the video or images to the user device 110 over the network link 416.
  • a flow cache entry is created for the flow in the flow cache 322.
  • the flow cache entry keeps track of the flow and its associated bandwidth.
  • the flow cache 322 updates the number of bytes for transmitted in the flow. By monitoring the number of bytes per flow over time, the flow analyzer 312 is capable of determining an estimate value of bandwidth associated with flow.
  • the TCP congestion control mechanism kicks in at the steering device 130, which may slows down and/or eventually stop receiving data over the network link 413 from origin server 160.
  • the steering device 130 would not forward any data to the network controller 140, since the link 416 is congested and the network controller 140 would not be able to transmit data to the user device 110. Therefore, as an inline element, the network controller 140 can detect network congestions and estimate bandwidth associated with any flows of interest selected by the network controller 140.
  • the network controller 140 does not modify and transform the HTTP messaged it receives over the ICAP interface. The network controller 140 simply updates the flow statistics and returns the video or images to the steering device 130 for transmission to the user device 110.
  • the network controller 140 can also aggregate the flows associated with a user or subscriber in order to estimate the total available bandwidth occupied by the user or subscriber.
  • the network controller 140 tracks all the flow cache entries looking for flows originated from a common source IP address or a user device identifier.
  • the flow analyzer 312 of the network controller 140 attempts to group these flows together to form a flow history for the user or subscriber.
  • the network controller further identifies users or subscribers using two data components in the flow cache entry: the TCP source port and HTTP cookies associated with the flow. Together with the flow history, the network controller 140 establish pattern, and identify users or subscribers and stores subscriber information in the subscriber log 324. More details of the flow cache and user mapping are described below with reference to FIG.
  • FIG. 4B illustrates one embodiment of a second example working mode of the network controller 140 for providing selective on-demand network monitoring.
  • the network request path consists of a network link 421 from the user device 110 to the steering device 130, and a network link 422 from the steering device 130 to the origin server 160.
  • the network response path consists of a network link 423 from the origin server 160 to the steering device 130, and a network link 424 from the steering device 130 back to the user device 110. Note that the network link pair 421 and 424 share the same physical link, so are network link pair 425 and 426.
  • the network controller 140 Similar to the "continue” mode, after receiving the initial HTTP messages of a flow and determining to monitor the flow, the network controller 140 notify the steering device 130 to work in a "counting" mode for bandwidth monitoring. In contrast to the “continue” mode, when a matching flow is detected for "counting" mode, the steering device 130 forwards the HTTP response directly to the user device 110. While at the same time, the steering device 130 send a customized ICAP message to the network controller 140 over the network link 425. In one embodiment, the customized ICAP message contains the HTTP request and response headers, as well as a count of payload size of the current flow. After updating the flow statistics, the network controller 140 may acknowledge the gateway over the network line 426.
  • the network controller 140 does not join the network response path as an inline network element, but simply listens to the counting of flow size.
  • the benefit of the "counting" mode is to off-load the network controller 140 from ingesting and forwarding the network flow on the network response path, while still enabling the detection of congestions and estimation of bandwidth associated with the flows of interest.
  • FIG. 5 is a block diagram illustrating an example event trace of "continue" working mode between the user device 110, steering device 130, network controller 140, video optimizer 150, and origin server 160.
  • the process starts when the user device 110 initiates an HTTP GET request 512 to retrieve content from the origin server 160.
  • the steering device 130 intercepts all requests originated from the user device 110. In one embodiment, the steering device 130 forwards the HTTP get request 512 to the intended origin server 160 and receives a response 514 back from the origin server 160.
  • the steering device 130 then sends an ICAP request message 516 comprising the HTTP GET request header and a portion of the response payload to the network controller 140, which inspects the message to determine whether to monitor the flow or optimize the video.
  • the network controller 140 responds with a redirect to optimize the video in ICAP response 518.
  • the steering device 130 re-writes the response 514 to an HTTP redirect response 520, causing the user device 110 to request the video file from the video optimizer 150.
  • the network controller 140 sends the HTTP redirect request 520 directly to the user device 110.
  • the steering device 130 would forward the response to the user device 110.
  • the user device 110 sends the request over the network to the video optimizer 150.
  • the network controller 140 monitors the traffic and/or requests from the client device 110 as the HTTP redirect request 520 is routed to the video optimizer 150.
  • the video optimizer 150 only sees requests for video files that need to be transcoded (e.q., optimized) and are associated with a HTTP redirect request 520. As such, the video optimizer 150 is not burdened with all the requests generated by a user device 110.
  • the video optimizer 150 forwards the video HTTP GET requests 522 to the origin server 160 and in return, receives a video file 524 from the origin server 160.
  • the video optimizer 150 transcodes the video file to a format usable by the client device 110 based on network bandwidth available to the user device 110.
  • the optimized video 526 is then transmitted from the video optimizer 150 to the steering device 130.
  • the steering device 130 intercepts the optimized video 526. Since the network controller 140 determines to monitor the flow in "continue" mode, the optimized video 526 is passed to the network controller 140 before it is returned to the steering device 130 and finally sent to the user device 110.
  • the client receives the optimized video 512 for substantially real-time playback on an application executing on the user device 110.
  • the video optimizer responsive to an HTTP get request 522 to an origin server 160, receives a HTTP 404 error from the origin server 160 as opposed to a video file.
  • the video optimizer 150 appends a "do not transcode" flag to the HTTP redirect request and returned to the user device 110, which re-sends the request out over the network to the origin server 160.
  • the origin server 160 responds appropriately to the request by sending back video 524, which is intercepted by the steering device 130 and the inline on-demand element the network controller 140 for monitoring purpose.
  • FIG. 6 is a block diagram illustrating an example event trace of "counting" working mode between the user device 110, steering device 130, network controller 140, video optimizer 150, and origin server 160.
  • the process starts when the user device 110 initiates an HTTP GET request 612 to retrieve content from the origin server 160.
  • the steering device 130 intercepts all requests originated from the user device 110. In one embodiment, the steering device 130 forwards the HTTP get request 612 to the intended origin server 160 and receives a response 614 back from the origin server 160.
  • the steering device 130 then sends an ICAP request message 616 comprising the HTTP GET request header and a portion of the response payload to the network controller 140, which inspects the message to determine whether to monitor the flow or optimize the video.
  • the network controller 140 responds with a redirect to optimize the video in ICAP response 618.
  • the steering device 130 re-writes the response 614 to an HTTP redirect response 620, causing the user device 110 to request the video file from the video optimizer 150.
  • the network controller 140 sends the HTTP redirect request 620 directly to the user device 110.
  • the steering device 130 would forward the response to the user device 110.
  • the user device 110 sends the request over the network to the video optimizer 150.
  • the network controller 140 monitors the traffic and/or requests from the client device 110 as the HTTP redirect request 620 is routed to the video optimizer 150.
  • the video optimizer 150 only sees requests for video files that need to be transcoded (i.e., optimized) and are associated with a HTTP redirect request 620. As such, the video optimizer 150 is not burdened with all the requests generated by a user device 110.
  • the video optimizer 150 forwards the video HTTP GET requests 622 to the origin server 160 and in return, receives a video file 624 from the origin server 160.
  • the video optimizer 150 transcodes the video file to a format usable by the client device 110 based on network bandwidth available to the user device 110.
  • the optimized video 626 is then transmitted from the video optimizer 150 to the steering device 130.
  • the steering device 130 intercepts the optimized video 626.
  • the steering device 130 will then send an ICAP request to the network controller 140 for inspection.
  • the network controller 140 deems this flow to be monitored and sends ICAP response 630.
  • the steering device 130 then allows the flow to go through to the user device 110.
  • the steering device 130 next sends periodic ICAP "counting" updates 632 to the network controller 140 until the flow completes.
  • the client receives the optimized video 626 for substantially real-time playback on an application executing on the user device 110.
  • the video optimizer 150 if the video optimizer 150 failed to retrieve user requested video file from the origin server 160, the video optimizer 150 appends a "do not transcode" flag to the HTTP redirect request and returned to the user device 110, which re-sends the request out over the network to the origin server 160.
  • the origin server 160 responds appropriately to the request by sending back video 624, which is intercepted by the steering device 130 only.
  • the steering device 130 forwards the video to the user device 110 and at the same time reports the flow size to the network controller 140 for monitoring purpose.
  • FIG. 7 is a block diagram illustrating one embodiment of an example of internal components of the flow cache.
  • the flow cache map 700 comprises a plurality of flow cache entries, such as flow cache entries 710 and 712 indexed by a hash. Not shown in the example diagram is a possible linked list behind each flow cache entry which allows chaining of flow cache entries for a given hash index.
  • the hash into the flow cache may be based on source IP address, MAC address, subscriber ID, or other identifier indicative of a given subscriber, group of subscribers or subscriber's device.
  • a flow cache block 720 pointed to by the flow cache entry 712 is shown to include information on source IP 722, one or more user flow blocks, which represent a logical group of flows associated with a user, a subscriber, or an entity representing a potential subscriber. Examples of these user flow blocks are default user flows block 724 and mapped user flows block 726.
  • the default user flows block 724 store flows that are not yet associated with any particular user or subscriber. If the subscriber id or any other identifiers associated with a particular user is known a-priori, all the flows associated with the particular user or subscriber will be assigned to the mapped user flows block 726.
  • the mapped user flows block 726 also include flows that either have been, or are in the process of being mapped to a user or subscriber by the flow analyzer 312. The mapped user flows block 726 can be indexed using subscriber id.
  • a flow can be assigned to the mapped user flows block 726 for a user or subscriber by the user's source IP address.
  • flows associated with an IP address may often be associated with a group of users or subscribers, but there is not enough information to identify a particular user or subscriber.
  • a pseudo subscriber id can be assigned in the default user flows block 724 until real users or subscribers are identified as more flows are observed.
  • An example user flow block 730 that can be included in the default user flows block 724 and the mapped user flows block 726 contains data fields like the subscriber id 732 (pseudo or real) estimated bandwidth 734, a list of all flows 736 associated with the subscriber id 732, and a list of cookie hashes 738 among other related flow information. Each entry in the list of cookie hashes 738 contains one unique cookie seen within the flows.
  • the list of flows 736 includes one or more flow statistics block 740.
  • Each flow statistics block 740 contains the IP flow identifier 742 (e.g., srcIP, dstIP, srcPort, dstPort), current domain and cookie 744, total number of bytes seen in each direction 746, the total number of bytes in each direction as of the last update 748. Not shown in the FIG. 7 includes a list of cookie hashes associated with the flow and an expiration time.
  • IP flow identifier 742 e.g., srcIP, dstIP, srcPort, dstPort
  • the flow cache map 700 can be split into multiple physical cache maps represented by one logical cache map. Due to large number of entries (e.g., millions of entries) and frequent accesses (e.g., thousands of times per second) to the logical caches, each split physical cache map needs to be managed and protected by its own thread against race conditions. The benefit of splitting the logical cache into multiple physical caches is to dramatically reduce the resource contention and other concurrency mishaps.
  • the flow cache map 700 can be split in various ways, such as selecting entries for the one or more physical flow caches based on the one or more bits from the least significant bits (LSBs) of the hash index. For example, if the lowest three bits of the hash index are used as an index, all the flow entries share the same three LSBs are organized into one of eight physical flow cache maps. To search within the flow cache map, hashes of the rest of the bits are used.
  • LSBs least significant bits
  • the estimated bandwidth 734 in a user flow block 700 can be calculated as follows.
  • each time a user flow block 730 is created or updated in the flow cache it is marked with a "dirty" flag.
  • the flag serves as an indicator to the flow analyzer 312 that the bandwidth may need to be recalculated for the flow so that the flow analyzer 312 does not have to traverse every user flow block to find out if it needs updating.
  • the "dirty" flag can be set in the user flow block 730 or in the flow statistics block 740.
  • the recalculation or update of the bandwidth can be performed periodically (e.g., in an interval of one second, ten seconds, or a minute).
  • the difference in transmitted (and/or received) bytes since the last update is used to measure bytes over time, percentage of consumed bandwidth compared to the overall bandwidth, the receive (rx) direction or the transmit (tx) direction bandwidth during the interval.
  • the transmitted (and/or received) bytes in the interval can also be aggregated for all the flows associated with a particular user to measure the estimated bandwidth the particular user receives.
  • flows are categorized into buckets based on the size of the objects being transferred. Small objects may not be factored into the bandwidth calculation since they may come and go within a single interval. For example, flows with payload size less than 50kB may be ignored because a transfer of 50kB may never reach the full potential throughput of the link. While larger flows may reach the full throughput of the link for a long period of time intervals, they are grouped into 50-75kB, 75-100kB and 100kB+ buckets because the characteristics of these flow sizes can be different, hence the bandwidth for each of the buckets is measured and calculated separately.
  • the flow size ranges (e.g., 50-75kB, 75-100kB and 100kB+) of the buckets may be altered depending on the network traffic and size of objects transmitted.
  • the bucket sizes can also be adjusted based on network topology, such as buffer size, prior to transmission to the client.
  • the calculated bandwidth per bucket is stored in a queue structure that allows for the computing and updating of minimum, maximum, and/or average measurements for each bucket.
  • the 100kB+ bucket's current tail entry is checked against the average bandwidth for the 100kB+ bucket. If the current entry is less than the average multiplied by the number of entries in the queue, the current entry is added to the bandwidth calculation for the current interval. This scheme can filter out large bursts of data from temporarily idle flows. If the bandwidth exceeds the value, a number of bytes (e.g., 125kB) will be subtracted from the current entry to account for TCP buffers in the network.
  • a number of bytes e.g., 125kB
  • the bandwidth for each of the buckets is calculated by combining the measured data sent for all the flows in the respective bucket.
  • flow sizes in the 100kB+ bucket is compared to a max burst value first. If a flow in the 100kB+ bucket is larger than a certain percentage (e.g., 25%) of the max burst value, the flow is not factored into the estimated bandwidth. Specifically, when a flow has been idle for a time, a large amount of data can burst before the network backs up due to network buffers. The max burst value keeps track of the largest bandwidth value seen for 100kB+ flows (or any highest bandwidth buckets) for a given user. If the flow size is much less than (e.g., less than 25%) the max burst value, it is added to the estimated bandwidth.
  • the estimated bandwidth is calculated by summing the bandwidth estimated from each of the buckets for the current interval.
  • the estimated bandwidth is only update if there is data sent during the interval, i.e. one or more buckets are not empty.
  • a minimum number of bytes needs to be sent in order to update the estimated bandwidth. This is done by weighting the average bandwidth per bucket multiplied by the bucket's lower byte limit (e.g. 50, 75, or 100kB) to put more weight on data from the largest buckets. Only when the combined weight from each of the buckets exceeds a threshold value, is the estimated bandwidth updated so as to prevent small bursts of data on the smaller buckets from creating spurious bandwidth updates.
  • the measured bandwidth is stored in a queue data structure. Each time a non-zero bandwidth is measured in an interval, the measured bandwidth is pushed into the tail of the queue. Estimated bandwidth can be calculated from an average, a maximum or a minimum value from the queue. The depth of the queue can be adjusted: more entries can smooth out variations in the estimated bandwidth, but slower to react to quick changes in the network (e.g., a small to medium size file being downloaded quickly). While fewer entries enables a faster reaction time, but reduces the ability to track longer term changes in the network (e.g., a medium to large size file being downloaded in addition to some smaller files). In one example, the queue depth may be capped between 10 and 30 entries. In another example, a sliding window technique is used in conjunction with a longer queue to smooth out variation as well as to track quick changes.
  • flow cache entries are searched by matching source IP address 722 if the subscriber id or other identifiers of the flow are not available.
  • the flow analyzer 312 needs to find patterns or other identifiers in the flows to map them to particular subscribers. Flows without identified subscribers are added to the flow cache block under the default user flows 726, which is a default holding place for the new flows.
  • the flow analyzer 312 later will scan through the default user flows that contain cookies or other identifiers that may be used to determine a real user or subscriber associated with the flow. If a flow contains identifiers not associated with an existing real user, a new user or subscriber is created and the user flow block is moved to newly created (or mapped) user or subscriber.
  • the flow analyzer 312 can also map flows to users (subscribers to the mobile or network service) in the flow cache entries by matching cookie hashes, MAC address (or any unique device identifiers), or TCP source ports. For example, if two flows share the same source port, it is very likely that they belong to the same user because TCP ports are reused often by an individual user, but not often between users.
  • source ports can also be used to map users when network address translation (NAT) is deployed.
  • NAT network address translation
  • each user is allocated a block (e.g., 32) of TCP source ports. A random port number within the block is then picked for each new user flows initiated. With this knowledge, all source ports within a block can be aggregated under the same user. In some cases, a user with more than one block of port number assigned, the cookie hashes can be used to link the blocks together.
  • a user flow block may be migrated from a current user to a better-matched user. This is only done in the direction from users with fewer associated flows to users with more associated flows to prevent moving flows back and forth between two users (i.e., oscillations). If all flows have been removed from a user, the user itself is removed.
  • the flow properties may be combined, for example, by summing up the byte counts, combining the cookie hashes, and extending the expiration time to a later time.
  • the subscriber log 324 stores all the mapped users.
  • existing users are organized into a database table indexed by identifiers, such as source port and cookie hash values.
  • migrating user flow block may be simply performed by unlinking the user flow block from the current user and linking it to the newly mapped user.
  • the system As in the case of table management, it is desirable to prevent the system from trying to do the user mapping across the entire flow cache in one go around. Thus, we can limit the number of user to process per pass and pick up any additional users on the next pass. As noted earlier, the identifiers may not be matched directly, but based on a hash thereof to save computation time.
  • Embodiments of the invention support a clientless video optimizer.
  • the video optimizer In the clientless mode, the video optimizer relies on the network controller to measure how much backpressure exists in the network so that the video optimizer can adjust the video bitrate accordingly.
  • the video optimizer acts as an on-demand proxy that the network controller invokes with an HTTP redirect request.
  • the network controller 140 with reference to FIG. 1 redirects the user device 110 to the video optimizer 150 to get the optimized video, which the video optimizer 150 retrieves from the origin server 160 specified in the redirect message.
  • the optimized video will be streamed to the user device.
  • the video optimizer 140 can monitor how much data it is able to push through the network.
  • the video optimizer may include a ring buffer where video produced is stored before being sent through the network to the user device.
  • the video optimizer keeps track of audio/video timestamps of the last data written and read from the ring buffer. If the network is backing up, data will back up in the ring buffer. By measuring the difference in timestamps of the first and last data in the ring buffer, the total latency of the network can be estimated.
  • the video optimizer generates the audio/video stream in real-time, so nominally the ring buffer should remain empty. If the stored latency in the ring buffer exceeds a certain amount (e.g., there seconds), the video optimizer may begin reducing the encoder bitrate in the audio/video encoder.
  • the amount of reduction of bitrate may be performed at a rate proportional to the latency.
  • the bitrate of video in the buffer will be reduced more aggressively if the buffer continues to fill.
  • the video optimizer will increase the bitrate by a fraction of the current bitrate.
  • Another mode of the video optimizer 150 looks for runaway bitrates. If the data rate coming out of the ring buffer is significantly less than the current encoder bit rate, the encoder bit rate can be reduced. This is useful because sometime a given audio/video sequence does not require a lot of bits to encode. The bit rate can be increased because the latency is staying low. However, the sequence may change and a lot of bits may be required, which can cause an overshoot in the encoding algorithm. In turn, the system may update the bitrates every few seconds (e.g., 5) to prevent rapid swings in video quality. The amount of the bit rate to be adjusted up and down is also regulated to prevent sudden quality changes.
  • the video optimizer 150 is also pacing the transcoded video output, which complicates the bandwidth measurement since the average network bandwidth becomes the pacing rate rather than the real network capacity.
  • the video optimizer 150 can burst data output in blocks by holding output data for a number of seconds and/or a number of bytes before outputting any data to the network. Once the holding period is over and/or the number of bytes held has been reached, the video optimizer 150 transmits the data in a burst to the network at a maximum speed. This allows the video optimizer and the network controller to correctly measure the network peak capacity.
  • the video optimizer 150 can track the average bandwidth output to the network without counting the holding periods when no data is being transmitted.
  • the network controller 140 plus the video optimizer 150 solution brings a break-through instant adaptation engine that can optimize nearly any video or image object in milliseconds.
  • the engine features format coverage (e.g., Flash, MP4, and ABR video) and delivers an average of 60% data savings on video and 50% on images, together providing an average 35% reduction in total traffic for typical mobile networks. This 35% reduction is calculated during peak network usage, which drives down capital and operating expenditures as opposed to savings due to pacing/throttling of the network traffic, which does not change the capital expenditure curve.
  • This solution is easy to deploy, maintain, and scale across a broad types of networks by blending cloud computing power and existing intelligent routing in the network.
  • embodiments of the invention provide a method and system for detecting and mitigating congestion in the mobile operator's network.
  • the system utilizes information from the network and flow-by-flow level statistics. Optimization can be triggered at bottleneck points of the network when it is needed most, i.e., when adverse network conditions, such as congestions, are detected or anticipated at times of peak load and/or based on the subscribers utilizing a particular portion of the network.
  • the system provides broad format and protocol coverage, full transcoding support, and flexible policies (e.g., time-of-day based optimization) that maximize both optimization efficiency and quality of experience, while significantly reducing costs and serving more subscribers on the same capital equipment base.
  • Modules may constitute either software modules (e.g., code embodied on a machine-readable medium or in a transmission signal) or hardware modules.
  • a hardware module is tangible unit capable of performing certain operations and may be configured or arranged in a certain manner.
  • one or more computer systems e.g., a standalone, client or server computer system
  • one or more hardware modules of a computer system e.g., a processor or a group of processors 102
  • software e.g., an application or application portion
  • a hardware module may be implemented mechanically or electronically.
  • a hardware module may comprise dedicated circuitry or logic that is permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations.
  • a hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.
  • processors e.g., processor 102
  • processors may be temporarily configured (e.g., by software) or permanently configured to perform the relevant operations.
  • processors may constitute processor-implemented modules that operate to perform one or more operations or functions.
  • the modules referred to herein may, in some example embodiments, comprise processor-implemented modules.
  • any reference to "one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment.
  • the appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
  • Coupled and “connected” along with their derivatives.
  • some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact.
  • the term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other.
  • the embodiments are not limited in this context.
  • the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion.
  • a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
  • “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

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Claims (15)

  1. Verfahren zum Abschätzen eines Bandbreitenverbrauchs in einem Dienstanbieternetzwerk, wobei das Verfahren Folgendes umfasst:
    Empfangen eines Hinweises für einen Beginn eines Netzwerkdatenflusses zwischen einem Ursprungsserver (160) und einem Benutzergerät (110), wobei der Datenfluss dazu dient, als Reaktion auf eine Anforderung nach Medieninhalt von einem anfordernden Benutzergerät (110) Medieninhalt von dem Ursprungsserver (160) an das Benutzergerät (110) zu liefern;
    Bestimmen, ob der Datenfluss zwischen dem Ursprungsserver (160) und dem Benutzergerät (110) überwacht werden soll;
    als Reaktion auf ein Bestimmen, den Datenfluss zu überwachen, Sammeln von statistischen Informationen, die dem Datenfluss entsprechen;
    Speichern der für den Datenfluss gesammelten statistischen Informationen in einem Flussdatensatz in einer Datenbank;
    Zuordnen des Flussdatensatzes in der Datenbank einem Teilnehmer des Dienstanbieternetzwerks auf der Grundlage einer Analyse der für den Datenfluss gesammelten statistischen Informationen, wobei der Zuordnungsschritt das Aggregieren von zugeordneten Flussdatensätzen für den Teilnehmer umfasst;
    Bestimmen von historischer Bandbreite, die dem Teilnehmer bereitgestellt worden ist, auf der Grundlage der für den Teilnehmer aggregierten zugeordneten Flussdatensätze; und
    Abschätzen verfügbarer Bandbreite auf dem Dienstanbieternetzwerk für den Datenfluss zwischen dem Benutzergerät (110) und dem Ursprungsserver (160) auf der Grundlage eines Vergleichs der für den Datenfluss gesammelten statistischen Informationen und der dem Teilnehmer bereitgestellten historischen Bandbreite.
  2. Verfahren nach Anspruch 1, wobei das Bestimmen, ob der Datenfluss überwacht werden soll, das Bestimmen umfasst, ob der Medieninhalt im Datenfluss auf eine Übertragung von Video und/oder Bildern hinweist, die größer als eine vorgegebene Größe sind.
  3. Verfahren nach einem der vorhergehenden Ansprüche, wobei die für den Datenfluss gesammelten statistischen Informationen eine Größe des zu liefernden Medieninhalts und einen Durchsatz des Datenflusses enthalten.
  4. Verfahren nach einem der vorhergehenden Ansprüche, wobei der Flussdatensatz Folgendes umfasst: Benutzer-Quell-IP-Adresse, Teilnehmeridentifikator, geschätzte Bandbreite, Cookie-Hashes, Flussidentifikator und/oder übertragene und empfangene Bytes, und wobei der Flussdatensatz während einer Lebensdauer des Datenflusses aktualisiert wird.
  5. Verfahren nach einem der vorhergehenden Ansprüche, wobei das Abschätzen der verfügbaren Bandbreite auf dem Dienstanbieternetzwerk für den Datenfluss Folgendes umfasst:
    Empfangen einer Aktualisierung von übertragenen oder empfangenen Bytes auf dem Flussdatensatz; und
    Berechnen der Bandbreite durch Teilen einer Differenz zwischen den übertragenen oder empfangenen Bytes seit einer letzten Aktualisierung durch einen Zeitabstand seit der letzten Aktualisierung.
  6. Verfahren nach einem der vorhergehenden Ansprüche, ferner Folgendes umfassend:
    Zuordnen des Flussdatensatzes einem Teilnetzwerkelement, das das Benutzergerät (110) im Dienstanbieternetzwerk nutzt; und
    Bestimmen einer Überlastungsstufe auf dem Teilnetzwerkelement für den Datenfluss auf der Grundlage eines historischen Durchschnitts pro Benutzerbandbreite auf dem Teilnetzwerkelement.
  7. Verfahren nach Anspruch 6, ferner Folgendes umfassend:
    Bestimmen, ob der Datenfluss auf der Grundlage der Überlastungsstufe auf dem Teilnetzwerkelement des Dienstanbieternetzwerks und einer Größe des im Datenfluss zu liefernden Medieninhalts optimiert werden soll; und
    Umleiten der Anforderung nach dem Medieninhalt von dem anfordernden Benutzergerät an einen Videooptimierer, wobei der Videooptimierer den Medieninhalt von dem Ursprungsserver abruft, den Medieninhalt für das Benutzergerät optimiert und den optimierten Medieninhalt an das Benutzergerät überträgt.
  8. Verfahren nach einem der vorhergehenden Ansprüche, wobei das Aggregieren der zugeordneten Flussdatensätze, die dem Teilnehmer des Dienstanbieternetzwerks entsprechen, auf dem Anpassen von Folgendem basiert: Benutzer-Quell-IP-Adresse und Mac-Adresse, TCP-Portnummer und/oder Hashes von einem mit dem Datenfluss verknüpften Cookie.
  9. Nicht flüchtiges computerlesbares Speichermedium, das ausführbare Computerprogrammanweisungen (224) zum Abschätzen von Bandbreitenverbrauch in einem Dienstanbieternetzwerk speichert, wobei die Computerprogrammanweisungen (224), wenn sie durch einen Prozessor (202) ausgeführt werden, bewirken, dass der Prozessor (202) Schritte durchführt, die Folgendes umfassen:
    Empfangen eines Hinweises auf einen Beginn eines Netzwerkdatenflusses zwischen einem Ursprungsserver (160) und einem Benutzergerät (110), wobei der Datenfluss dazu dient, als Reaktion auf eine Anforderung nach Medieninhalt von einem anfordernden Benutzergerät (110) Medieninhalt von dem Ursprungsserver (160) an das Benutzergerät (110) zu liefern;
    Bestimmen, ob der Datenfluss zwischen dem Ursprungsserver (160) und dem Benutzergerät (110) überwacht werden soll;
    als Reaktion auf ein Bestimmen, den Datenfluss zu überwachen, Sammeln von statistischen Informationen, die dem Datenfluss entsprechen;
    Speichern der für den Datenfluss gesammelten statistischen Informationen in einem Flussdatensatz in einer Datenbank;
    Zuordnen des Flussdatensatzes in der Datenbank einem Teilnehmer des Dienstanbieternetzwerks auf der Grundlage einer Analyse der für den Datenfluss gesammelten statistischen Informationen, wobei der Zuordnungsschritt das Aggregieren von zugeordneten Flussdatensätzen für den Teilnehmer umfasst;
    Bestimmen von historischer Bandbreite, die dem Teilnehmer bereitgestellt worden ist, auf der Grundlage der für den Teilnehmer aggregierten zugeordneten Flussdatensätze; und
    Abschätzen verfügbarer Bandbreite auf dem Dienstanbieternetzwerk für den Datenfluss zwischen dem Benutzergerät (110) und dem Ursprungsserver (160) auf der Grundlage eines Vergleichs der zum Datenfluss gesammelten statistischen Informationen und der dem Teilnehmer bereitgestellten historischen Bandbreite.
  10. Nicht flüchtiges computerlesbares Speichermedium nach Anspruch 9, wobei das Bestimmen, ob der Datenfluss überwacht werden soll, das Bestimmen umfasst, ob der Inhalt im Datenfluss Video und/oder Bilder enthält, die größer sind als eine vorgegebene Größe.
  11. Nicht flüchtiges computerlesbares Speichermedium nach einem der Ansprüche 9 oder 10, wobei die für den Datenfluss gesammelten statistischen Informationen eine Größe des zu liefernden Medieninhalts und einen Durchsatz des Datenflusses enthalten.
  12. Nicht flüchtiges computerlesbares Speichermedium nach einem der Ansprüche 9, 10 oder 11, wobei die Anweisungen (224), wenn sie durch den Prozessor (202) ausgeführt werden, ferner bewirken, dass der Prozessor (202) Schritte durchführt, die Folgendes umfassen:
    Zuordnen des Flussdatensatzes einem Teilnetzwerkelement, das das Benutzergerät (110) im Dienstanbieternetzwerk nutzt; und
    Bestimmen einer Überlastungsstufe auf dem Teilnetzwerkelement für den Datenfluss auf der Grundlage eines historischen Durchschnitts pro Benutzerbandbreite auf dem Teilnetzwerkelement.
  13. Netzwerkcontroller (140) zum Abschätzen von Bandbreitenverbrauch in einem Dienstanbieternetzwerk, Folgendes umfassend:
    einen Prozessor (202); und
    ein nicht flüchtiges computerlesbares Speichermedium, das ausführbare Computerprogrammanweisungen (224) speichert, die, wenn sie durch den Prozessor (202) ausgeführt werden, bewirken, dass der Prozessor (202) Schritte durchführt, die Folgendes umfassen:
    Empfangen eines Hinweises für einen Beginn eines Netzwerkdatenflusses zwischen einem Ursprungsserver (160) und einem Benutzergerät (110), wobei der Datenfluss dazu dient, als Reaktion auf eine Anforderung nach Medieninhalt von einem anfordernden Benutzergerät (110) Medieninhalt von dem Ursprungsserver (160) an das Benutzergerät (110) zu liefern;
    Bestimmen, ob der Datenfluss zwischen dem Ursprungsserver (160) und dem Benutzergerät (110) überwacht werden soll;
    als Reaktion auf ein Bestimmen, den Datenfluss zu überwachen, Sammeln von statistischen Informationen, die dem Datenfluss entsprechen;
    Speichern der für den Datenfluss gesammelten statistischen Informationen in einem Flussdatensatz in einer Datenbank;
    Zuordnen des Flussdatensatzes in der Datenbank einem Teilnehmer des Dienstanbieternetzwerks auf der Grundlage einer Analyse der für den Datenfluss gesammelten statistischen Informationen, wobei das Zuordnen des Flussdatensatzes das Aggregieren der zugeordneten Flussdatensätze für den Teilnehmer umfasst;
    Bestimmen von historischer Bandbreite, die dem Teilnehmer bereitgestellt worden ist, auf der Grundlage der für den Teilnehmer aggregierten zugeordneten Flussdatensätze; und
    Abschätzen verfügbarer Bandbreite auf dem Dienstanbieternetzwerk für den Datenfluss zwischen dem Benutzergerät (110) und dem Ursprungsserver (160) auf der Grundlage eines Vergleichs der zum Datenfluss gesammelten statistischen Informationen und der dem Teilnehmer bereitgestellten historischen Bandbreite.
  14. System, umfassend den Netzwerkcontroller (140) nach Anspruch 13 und eine Lenkvorrichtung (130), die kommunikativ an den Netzwerkcontroller (140) gekoppelt ist, wobei die Lenkvorrichtung (130) konfiguriert ist, den Datenfluss als Reaktion auf das Bestimmen, den Datenfluss zu überwachen, durch den Netzwerkcontroller (140) zu leiten, und
    der Netzwerkcontroller (140) konfiguriert ist, den Datenfluss von der Lenkvorrichtung (130) zu empfangen und die dem Datenfluss entsprechenden statistischen Informationen zu sammeln und den Datenfluss zurück an die Lenkvorrichtung (130) weiterzuleiten.
  15. System, umfassend den Netzwerkcontroller (140) nach Anspruch 13 und eine Lenkvorrichtung (130), die kommunikativ an den Netzwerkcontroller (140) gekoppelt ist, wobei der Netzwerkcontroller (140) konfiguriert ist, die dem Datenfluss entsprechenden statistischen Informationen von der Lenkvorrichtung (130) zu sammeln; und
    die Lenkvorrichtung (130) konfiguriert ist, die dem Datenfluss entsprechenden statistischen Informationen dem Netzwerkcontroller (140) bereitzustellen, während der Datenfluss zwischen dem Ursprungsserver (160) und dem Benutzergerät (110) empfangen und weitergeleitet wird.
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RU2585971C1 (ru) 2016-06-10
CN104488231A (zh) 2015-04-01
KR101578473B1 (ko) 2015-12-17
EP2856710A1 (de) 2015-04-08
WO2013181654A1 (en) 2013-12-05
US9331944B2 (en) 2016-05-03
US8792347B2 (en) 2014-07-29
KR20150032541A (ko) 2015-03-26
CN104488231B (zh) 2017-06-16
US20140254380A1 (en) 2014-09-11
US20130322242A1 (en) 2013-12-05
EP2856710A4 (de) 2016-03-02

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